Correcting Positional Errors in Shore-Based Theodolite Measurements of Animals at Sea
Bibliographic record
Abstract
Determining the position of animals at sea can be particularly difficult and yet, accurate range and position of animals at sea are essential to answer a wide range of biological questions. Shore-based theodolite techniques have been used in a number of studies to examine marine mammal movement patterns and habitat use, offering reliable position measurements. In this study we explored the accuracy of theodolite measurements by comparing positional information of the same objects using two independent techniques: a shore-based theodolite station and an onboard GPS over a range of 25 km from the shore-based station. The technique was developed to study the habitat use of sperm whales (Physeter macrocephalus) off Kaikoura, New Zealand. We observed that the position accuracy fell rapidly with an increase in range from the shore-based station. Results showed that the horizontal angle was accurately determined, but this was not the case for the vertical angle. We calibrated the position of objects at sea with a regression-based correction to fit the difference in distance between simultaneously recorded theodolite fixes and GPS positions. This approach revealed the necessity to calibrate theodolite measurements with objects at sea of known position.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".